Advancements in digital technologies greatly enhance performance of devices and services to improve the well-being of community citizens. Many of these advancements include systems that rely on the use of sensors to monitor circumstances in specific geographic locations. Intelligent data produced by these sensors (e.g., thermometers, cameras) and correlated with their specific locations, is integrated with tools able to determine when problems arise at these unique locations. The tools enable users to make informed decisions to mitigate the problems or enhance their environment.
Various combinations of sensors can be used to monitor existing or future problems (i.e., water, air and noise pollution, traffic). For example, sensing devices, such as video cameras, can be installed on lampposts along streets to monitor various pedestrian and automobile traffic conditions. When accurately correlated with specific geo-locations, this data provides intelligent information to cities and to the customers about detecting vehicles, parking availability, monitoring traffic anomalies, and detecting other problematic events. Accordingly, having these anomalies and events accurately reported in terms of their geo-location can be critical.
By way of example, a suspicious car parked along a street may be detected via a stationary (outdoor) video camera and may need to be reported to community authorities. In this instance, the location of the parked car must be autonomously reported with high accuracy (e.g., within a few feet).
To achieve this accuracy, the system must be capable of locating physical points (i.e. pixel coordinates) of interest within the camera's field of view, then finding the physical geo-location coordinates (e.g., latitude/longitude) of each of the points. Conventional approaches for locating these physical points and correlating these points with geo-location coordinates can be a time-consuming process.
For example, one conventional approach includes a manual process of identifying points of interest in a camera image and locating positions of corresponding points in the physical field of view, on maps, or on satellite images. This conventional manual approach, however, is not suitable for quickly and accurately obtaining him geo-location latitude and longitude of points of reference along the streets. This shortcoming is especially pronounced in cities including multiple high-rise buildings that may diminish the accuracy of the geo-location device.